Combine uncertainty estimates from multiple MC Dropout passes where each pass produces quantile bounds and a mean. For each observation, samples are drawn from Normal approximations of aleatoric noise across passes, yielding a predictive mixture distribution.
.combine_uncertainties_sampling(
lwr_mat,
upr_mat,
mean_mat,
alpha = 0.05,
inner_samples = 50,
centerline = NULL
)A data.frame with columns:
lwr, upr: lower/upper predictive interval.
var_epistemic: epistemic variance (across passes).
var_aleatoric: average aleatoric variance.
var_total: sum of epistemic and aleatoric variances.
Matrix [passes, n_obs] of lower quantile predictions.
Matrix [passes, n_obs] of upper quantile predictions.
Matrix [passes, n_obs] of mean predictions.
Coverage level (default 0.05).
Number of Normal samples per pass/observation.
Optional vector of deterministic mean predictions (overrides pass-mean).
Ines Ortega-Fernandez, Marta Sestelo